The human visual system has a remarkable ability to adapt to the ambient illumination in such a way that colors appear the same under different illumination conditions, even when physical measurements show that the spectrum of the light being received by the eye is quite different due to the different illumination conditions. For example, the leaves of a tree will be perceived largely as a particular shade of green regardless of whether the leaves are viewed in bright sunlight, the light from an overcast daylight, or in the reddish light of a sunset.
This ability of the human visual system poses a problem for artificial systems such as cameras, scanners or printers that wish to capture or represent an image of a scene under illumination conditions that are different to those under which the image of the scene was originally captured. For instance, if an image of a scene is captured by a camera at sunset which introduces a reddish illuminant, then the camera will record an image with a reddish tinge. The visual system of a human viewing that scene in situ will be adapted to the reddish light source, and will perceive colors of that scene as if they were under a much whiter light source, whereas a human viewing the image of the scene captured by the camera under different illumination conditions will not be adapted to a reddish light source, so the colors will be perceived as unnatural.
Historically, this problem has been ameliorated through the use of chromatic adaptation, and in particular a chromatic adaptation transform, which converts input colors captured under an first illuminant to corresponding output colors under a second illuminant Thus, a chromatic adaptation transform is applied to the tristimulus values (X′,Y′,Z′) of a color captured under a first illuminant in order to predict the corresponding color's tristimulus values (X″,Y″,Z″) under a second illuminant.
Two chromatic adaptation transforms in widespread use are those based on the von Kries and Bradford models. Both use a 3×3 matrix as chromatic adaptation transform of the tristimulus values (X′,Y′,Z′) of the color captured under the first illuminant to predict the corresponding tristimulus values (X″,Y″,Z″) under the second illuminant.
The performance of a chromatic adaptation transform may be determined by taking measurements of the color of a range of surfaces under different illumination conditions and comparing the predictions of the chromatic adaptation transform with the actual measurements taken under the different illumination conditions. It is a desirable property of a chromatic adaptation transform that the average of the differences between the predicted and actual measured colors be as small as possible. For instance, transforms based on the Bradford model outperforms transforms based on the von Kries model in their ability to more accurately predict colors for almost all illuminants and reflecting surfaces. However, transforms based on the Bradford model do not produce the smallest average difference between predicted and actual measured colors.
Furthermore, the transform based on the Bradford model is based in part on testing of human observers in its formulation. When the chromatic adaptation transform is intended to be used to transform colors captured by artificial observers, such as cameras and scanners, the fact that the transform based on the Bradford model is formulated through the testing of human observers, does not make it a natural choice.